Machine learning is an effective tool for optimizing the performance of large-scale networks under dynamic environments. One of the approaches is deep reinforcement learning (DRL), which is a combination of reinforcement learning and neural networks. In wireless networks, DRL algorithms are used for maximizing the network performance, reducing power consumption and improving the processing time for real-time applications [1]}, [2]}, [3]}. DRL algorithms are powerful in wireless networks because the agents do not need pre-collected data for training. Rather, DRL agents interact with their environment and establish training samples for the responses in those interactions. The neural networks are trained by up-to-date state transitions to adjust their parameters for maximizing a designated reward. Then, the trained networks are deployed for real-time prediction.
